Discrete Cosine Transform Algorithm Research Articles

Effect of Genetic Algorithm as a Feature Selection for Image Classification

Analysis of image content is important in the classification of images, identification, retrieval, and recognition processes. The medical image datasets for content-based medical image retrieval ( are large datasets that are limited by high computational costs and poor performance. The aim of the proposed method is to enhance this image retrieval and classification by using a genetic algorithm (GA) to choose the reduced features and dimensionality. This process was created in three stages. In the first stage, two algorithms are applied to extract the important features; the first algorithm is the Contrast Enhancement method and the second is a Discrete Cosine Transform algorithm. In the next stage, we used datasets of the medical images using GA-based feature selection to find feature vectors. Images from the datasets and images from the query are recognized using a correlation coefficient. The third stage of the proposed method used a diverse density algorithm feedback technique to enhance the performance of the . Images of breast cancer, brain cancer, lung cancer, thyroid cancer, etc., may be retrieved using the suggested procedure. By using a feature selection algorithm based on GA to determine the best subset of features, the challenge of system dimensionality is reduced. The suggested method has greater accuracy in precision, recall, and F-score than the other techniques.

Steganography a Technique to Hide Information within an Image

Steganography is the science and art of secret communication between two parties that seek to conceal the content of their messages. It is a technology that embeds information into cover photos without losing information after embedding. Steganography is the technique and method of hiding a message so that no one but the sender and receiver will suspect how the message survived. This article compares two different techniques. The first technique uses the least significant bit (LSB) without encryption or compression. Now in the second method, the encrypted message is first encrypted and then the least significant bit is used. The image is then transformed into the frequency domain using a Discrete Cosine Transform (DCT). The LSB algorithm is applied in the local domain where the image parts corresponding to the payload are embedded in the least significant parts of the overlay image and the image is embedded in the least significant parts of the overlay image to generate a steganographic image. The DCT algorithm is appliedin the frequency domain and transforms from the steganographic image domain to the spatial domain where the image title is interpolated to the least significant parts of the image. This white paper mainly provides an overview of steganography requirements benefits and related technologies. This white paper shows which steganographic methods are most effective for which requirements are met and which applications are most compatible with which steganographic methods.

Small-Size Algorithms for the Type-I Discrete Cosine Transform with Reduced Complexity

Discrete cosine transforms (DCTs) are widely used in intelligent electronic systems for data storage, processing, and transmission. The popularity of using these transformations, on the one hand, is explained by their unique properties and, on the other hand, by the availability of fast algorithms that minimize the computational and hardware complexity of their implementation. The type-I DCT has so far been perhaps the least popular, and there have been practically no publications on fast algorithms for its implementation. However, at present the situation has changed; therefore, the development of effective methods for implementing this type of DCT becomes an urgent task. This article proposes several algorithmic solutions for implementing type-I DCTs. A set of type-I DCT algorithms for small lengths N=2,3,4,5,6,7,8 is presented. The effectiveness of the proposed solutions is due to the possibility of fortunate factorization of the small-size DCT-I matrices, which reduces the complexity of implementing transformations of this type.

A new watermarking scheme for digital videos using DCT

Abstract With the advent of high-speed broadband Internet access, the need to protect digital videos is highly recommended. The main objective of this study is to propose an adaptive algorithm for watermarked digital videos in the frequency domain based on discrete cosine transform (DCT). The watermark signature image is embedded into the whole frame of the video. The green channel of the RGB frame is selected for the embedding process using the DCT algorithm as it shows the recommended quality of the watermarked frames. The experiment results indicate that the proposed algorithm shows robustness and high quality of the watermarked videos by testing various strength values ∆ for different videos. It offers resistance against different types of attacks.

Image Processing for Improvement of Facial Keypoints Detector

This paper discusses the improvement of facial detection algorithms using the DCT algorithm and image processing. Face key point is very needed in the face recognition system. Some important factors that have effects to detect its result are noise and illuminations. These two factors can be overcome by eliminating some DCT coefficients, both high and low. However, after handling the problem, most likely the image quality will become decrease, which will adversely influence the performance of the feature detector algorithm. Therefore, it is very important to test the performance of the feature detector algorithm on images that are implemented noise and illumination handling and how to improve the quality again. This research implemented Discrete Cosine Transform (DCT), by eliminating the high and low coefficient because there is noise and illumination. However, it is not known at what coefficient level is the most effective, so testing in this study was carried out. Four deblurring algorithms are tested in this research, Blind Deconvolution, Wiener Filter Deconvolution, Lucy-Richardson Deconvolution, and Regularization Deconvolution. And tested the CLAHE algorithm to overcome the effect of removing low coefficient DCT. The best coefficient value to be removed at the DCT frequency is 0.75 with the best SURF algorithm, without the use of other algorithms. Also, the highest F-score is produced by the SURF detector at removing DCT low frequency in combination with the CLAHE algorithm. With the most ideal coefficient of 0.25.

A secure ECG signal transmission for heart disease diagnosis

Due to the high sampling rate, the recorded Electrocardiograms (ECG) data are huge. For storing and transmitting ECG data, wide spaces and more bandwidth are therefore needed. The ECG data are also very important to preprocessing and compress so that it is distributed and processed with less bandwidth and less space effectively. This manuscript is aimed at creating an effective ECG compression method. The reported ECG data are processed first in the pre-processing unit (ProUnit) in this method. In this unit, ECG data have been standardized and segmented. The resulting ECG data would then be sent to the Compression Unit (CompUnit). The unit consists of an algorithm for lossy compression (LosyComp), with a lossless algorithm for compression (LossComp). The randomness ECG data is transformed into high randomness data by the failure compression algorithm. The data's high redundancy is then used with the LosyComp algorithm to reach a high compression ratio (CR) with no degradation. The LossComp algorithms recommended in this manuscript are the Discrete Cosine Transform (DCT) and Discrete Wavelet Transform (DWT). LossComp algorithms such as Arithmetic Encoding (Arithm) and Run Length Encoding (RLE) are also suggested. To evaluate the proposed method, we measure the Compression Time (CompTime), and Reconstruction Time (RecTime) (T), RMSE and CR. Simulation results suggest the highest output in compression ratio and in complexity by adding RLE after the DCT algorithm. The simulation findings indicate that the inclusion of RLE following the DCT algorithm increases performance in terms of CR and complexity. With CR = 55% with RMSE = 0:14 and above 94% with RMSE = 0:2, DCT as a LossComp algorithm was utilized initially, followed by RLE as a LossComp algorithm.

Design, optimisation and implementation of a DCT/IDCT-based image processing system on FPGA

In this paper, a Discrete Cosine Transform (DCT) and its inverse transform IDCT are designed and optimised for FPGA using the Xilinx VIVADO High-Level Synthesis (HLS) tool. The DCT and IDCT algorithms along with a filter logic written by C/C++ are simulated for functional verification and optimised through HLS and packaged as custom IPs. The IPs are incorporated into a VIVADO project to form an image processing system for hardware validation. The VIVADO design along with a Xilinx SDK application written by C language is implemented on a Zynq FPGA development board, ZedBoard. A C# GUI is developed to transfer image data to/from the FPGA and display the original and processed images. Experimental results are presented with discussion. The FPGA development method including the DCT/IDCT IP design, optimisation and implementation via HLS as well as the VIVADO project integration can be extended to a wider range of FPGA applications.

RETRACTED: Swimming target detection and tracking technology in video image processing

The new system is presented for target detection swimming, tracking multiple targets in a video sequence. Since the output of the system is based on target motion, to use background subtraction to move the area within each video frame of the first segment foreground and background. A new method proposed for detecting drowning in a person swimming in a data using video footage. The proposed Swimming target detection and tracking technology based on Discrete Cosine Transform Algorithm, Target Detection Algorithm, and Traditional Cam-Shift Algorithm. For background extraction and updating, use the frame-by-frame differential Discrete Cosine Transform Algorithm used to the exact motion region of the entire video section in image processing technology based analysis. Static and dynamic feature detection identification normal swimmers and drowning swimmers. Swimming-based data are collected in real time using a camera on the surface of the water, including a video image sequence with the main observation swimming and behavior analysis based system immersion and three steps to detect swimming. By combining frame subtraction and background subtraction, the advanced moving Target Detection Algorithm can detect the full area of interest. Then, with average particle filtering, analysis of Traditional Cam Shift Algorithm, it can automatically select the target for the resulting detection results and introduce a new person tracking method. Swim target detection and track results to analyze motion parameter estimation algorithms. Finally, the purpose of the set of human diagnostics and follow-up tests is to test algorithmic efficiency.

Recursive Algorithms for Computing Sliding DCT With Arbitrary Step

Discrete cosine transform (DCT) is one of the most popular transforms in digital signal processing. Its close approximation to the Karhunen-Loeve transform implies a high degree of energy compaction. Therefore, it can be used in a wide range of applications such as sensor noise removal, spectral analysis, linear filtering, feature extraction and pattern recognition. When it is required to estimate the spectrum of a nonstationary process such as radar, speech, biomedical and communication signals, a short-time (sliding) transform can be used. Basically, the sliding transform means that the transform is calculated on a fixed-length window of the signal, which is constantly updated with new samples while the oldest ones are discarded. In some engineering applications, when the spectral content changes slowly, the window can slide more than one sample at a time to speed up the spectral analysis. In this paper, second-order recursive algorithms are proposed for fast computing the DCT in windows that are located at a given equal distance from each other. The computational complexity of the algorithms is compared with that of common fast and sliding DCT algorithms.

A Secure and Privacy‐Preserving Watermark Based Medical Image Sharing Method

The patients' medical image data are one of the most important data in e-health. Medical image data usually play a crucial role in disease diagnosis and implicate unpredictable potential values for improving diagnostic methods and adjusting diagnostic results. To exploit their incredible potential values, medical images need to be shared among different hospitals, medical institutions and insurance companies and others. But how to securely and effectively share these medical image data becomes a challenging problem. In this paper, we proposed to combine encryption and digital watermark technology to achieve a secure and privacy-preserving medical image sharing method. The QR code image of the concatenation of authoritative diagnosis results and the hash of the original medical image is generated as the watermark image. The Discrete cosine transform (DCT) and Inverse DCT (IDCT) algorithms are utilized to embed the watermark image. As the watermarked medical images are desensitized, they are stored to a smart contract based blockchain, such as Ethereum, to achieve secure and fair sharing between data owners and users. The experimental results show that the proposed method can resist several attacks meanwhile it is efficient in medical image sharing.

Robust video watermarking using a hybrid DCT-DWT approach

In order to solve the limitations of the digital video watermarking algorithm, this paper proposes a new robust video watermarking algorithm using combining discrete cosine transform (DCT) and discrete wavelet transform (DWT) techniques. First of all, the video frames are randomly selected and then the DCT algorithm is applied to the selected video frames. After that, the first column of the selected video frames is scrambled using the Arnold algorithm. Furthermore, every column with 4 direct current (DC) coefficients is reshaped and transformed into four different sub-bands using the DWT technique. Next, the watermark is embedded into the approximation (LL) sub-band. The proposed algorithm is easy to carry out because it provides random frames with no special requirements for video frames. The experiment results indicate that this algorithm can resist against different kinds of watermarking attacks, such as the Gaussian filter attack and sharpen attack. In addition, it also illustrates that the proposed algorithm has a better result than some other watermarking algorithms.

A Digital Watermarking Encryption Technique Based on FPGA Cloud Accelerator

Digital watermarking has the properties such as invisibility and anti-aggression, so the digital watermarking technology has been widely used in copyright protection, information hiding. The watermarking technology takes into account the invisibility and robustness of the watermark by controlling the embedding intensity and position of the watermark mainly in the transformation domain. In this paper, discrete cosine transform (DCT) is adopted to transform the given image from spatial domain to frequency domain for adding watermark information. In order to meet the demands of image watermarking batch processing and cloud processing in the future, this paper optimized the DCT algorithm and the data precision, and successfully deployed the designed accelerator kernel on the FPGA cloud platform to speed up the processing of watermarking. The implementation of data processing based on cloud platform is the development trend of big data era. The cloud platform adopted in this paper is based on the OpenCL heterogeneous architecture combining CPU and FPGA. The cloud-based implementation makes digital watermarking application highly extensible, widely shareable, and more secure. The whole system implements a series of complete cloud processes including image decoding, image preprocessing, watermark embedding, and watermarked image encoding. The watermarking algorithm is accelerated by the efficient parallel computing capabilities of FPGA. It can be seen that the result of acceleration is remarkable, providing the state-of-the-art throughput of 1.676 GBps and the highest processing speed of 937 FPS for $800\times800$ sized colorimage.

A new image transmission compression approach based on Beidou navigation satellite system on the Open Sea

In order to solve the problem that a great amount of image information need to be transmitted on the mariculture of open sea and Beidou Navigation Satellite System (BDS) has the limited transmitting capability, this paper proposes a new compression approach to transmit image using BDS, i.e., the variable sequence progressive compression approach (VSPC). Firstly, by performing matrix operation and difference component extraction on the binary stream data, the data quantity is reduced. Then, the data quantity is further reduced by using the iterative compression approach. The simulation results show that the proposed approach can effectively reduce the amount of transmitted data, reduce the compression ratio and improve the data transmission efficiency under the premise of ensuring the same image quality by comparing with the traditional Huffman and DCT (Discrete Cosine Transform) algorithms.

Hiding data in images using steganography techniques with compression algorithms

Steganography is the science and art of secret communication between two sides that attempt to hide the content of the message. It is the science of embedding information into the cover image without causing a loss in the cover image after embedding.Steganography is the art and technology of writing hidden messages in such a manner that no person, apart from the sender and supposed recipient, suspects the lifestyles of the message. It is gaining huge attention these days as it does now not attract attention to its information's existence. In this paper, a comparison of two different techniques is given. The first technique used Least Significant Bit (LSB) with no encryption and no compression. In the second technique, the secret message is encrypted first then LSB technique is applied. Moreover, Discrete Cosine Transform (DCT) is used to transform the image into the frequency domain. The LSB algorithm is implemented in spatial domain in which the payload bits are inserted into the least significant bits of cover image to develop the stego-image while DCT algorithm is implemented in frequency domain in which the stego-image is transformed from spatial domain to the frequency domain and the payload bits are inserted into the frequency components of the cover image.The performance of these two techniques is evaluated on the basis of the parameters MSE and PSNR.

Computed tomogram image encoding for internet of things based systems

Background: In recent times, transmission of information over a wireless channel is increasing at an exponential rate. Applications based on IOT will exceed all form of data available over the internet, it can be in the form of data based on videos and images which alone size up to 70% of the global traffic.
 Methodology: In this paper, an encoding technique based on discrete cosine transform (DCT) have been utilized to reduce information of a CT image that is unwanted or a human eye is unable to perceive. For medical images, preserving maximum information in order to properly diagnose any disease is important and for such application, the amount of quantization needed to gain an image with minimum error is to observe the Correlation index (CI).
 Results: CI at 50% Quality Quantization Table (QQT) for image 1 was found to be 0.9981 and its size reduced to 0.403 MB, it means that the compressed image is 99.8% similar to the original image. But its similarity reduces with the increase in QQT, its size will also decrease by at this point quality will degrade too.
 Conclusion: In order to store images on the drive, it is imperative to apply a compression technique. In this paper, a compression technique is proposed utilizing a DCT algorithm to reduce an image size with different QQT provided for quality assessment. Each image has a different set of information and to cater their differences this QQT can be manipulated for best possible quality with acceptable size on disk.

Signal and image compression using quantum discrete cosine transform

The discrete cosine transform (DCT) is widely used in image and video compression standard formats. This is due to its ability to represent signals and images using a limited number of significant coefficients without noticeable loss of visual clarity. The classical one-dimensional discrete cosine transform (1D - DCT) and two-dimensional discrete cosine transform (2D - DCT) have computational complexities of O(Nlog2N) and O(N2log2N), respectively. Thus, as the images grow in size, the runtime of the DCT highly increases which could limit its usability in real-time applications. This paper presents a quantum DCT algorithm (QDCT) that is more efficient than its classical counterpart in terms of complexity. Furthermore, the proposed QDCT is used to develop and realize a quantum image compression technique. The developed compression technique performs a search to determine the most significant computed DCT coefficients and is derived from Grover’s algorithm. It provides a generalization to the original search algorithm by utilizing two oracle operators to solve the complex unstructured search problem rather than a single one. Thus, the proposed QDCT algorithm can simultaneously calculate the DCT coefficients and identify the significant DCT coefficients through applying two oracles. The comparison of the introduced QDCT with Grover’s algorithm also indicates that the QDCT algorithm is more efficient. This can be attributed to performing a rotation on the subspace rather than on the global space in Grover’s algorithm. In addition, the presented quantum 1D - and 2D - DCT have reduced complexities compared to the classical algorithms which are O(N) and O(N), respectively. Therefore, the presented QDCT and compression algorithm can be applied efficiently to accomplish various transform-based quantum signal and image processing tasks.

Design and Digital Implementation of Fast and Recursive DCT II–IV Algorithms

Using the proposed factorizations of discrete cosine transform (DCT) matrices, fast and recursive algorithms are stated. In this paper, signal flow graphs for the n-point DCT II and DCT IV algorithms are introduced. The proposed algorithms yield exactly the same results as with standard DCT algorithms but are faster. The arithmetic complexity and stability of the algorithms are explored, and improvements of these algorithms are compared with previously existing fast and stable DCT algorithms. A parallel hardware computing architecture for the DCT II algorithm is proposed. The computing architecture is first designed, simulated, and prototyped using a 40-nm Xilinx Virtex-6 FPGA and thereafter mapped to custom integrated circuit technology using 0.18- $$\upmu $$ m CMOS standard cells from Austria Micro Systems. The performance trade-off exists between computational precision, chip area, clock speed, and power consumption. This trade-off is explored in both FPGA and custom CMOS implementation spaces. An example FPGA implementation operates at clock frequencies in excess of 230 MHz for several values of system word size leading to real-time throughput levels better than 230 million 16-point DCTs per second. Custom CMOS-based results are subject to synthesis and place-and-route steps of the design flow. Physical silicon fabrication was not conducted due to prohibitive cost.

Realization of Second-Order Structure of Recursive Algorithm for Discrete Cosine Transform

A computational and hardware-efficient second-order infinite impulse response filter structure is proposed in this paper. It can compute discrete cosine transform (DCT) with improved processing speed and is valid for N = 2r, where N is the length of the input sequence and r > 1. A new algorithm is also proposed in this paper which is an improvement over previously reported algorithms in the literature. The proposed algorithm reduces the total number of real multiplications and additions in comparison with the existing algorithms. Using the suggested algorithm, computational cycles required to compute a DCT coefficient are less which further reduces the truncation error while processing a long length of input data.

Advancing the prediction accuracy of protein-protein interactions by utilizing evolutionary information from position-specific scoring matrix and ensemble classifier

Protein-Protein Interactions (PPIs) are essential to most biological processes and play a critical role in most cellular functions. With the development of high-throughput biological techniques and in silico methods, a large number of PPI data have been generated for various organisms, but many problems remain unsolved. These factors promoted the development of the in silico methods based on machine learning to predict PPIs. In this study, we propose a novel method by combining ensemble Rotation Forest (RF) classifier and Discrete Cosine Transform (DCT) algorithm to predict the interactions among proteins. Specifically, the protein amino acids sequence is transformed into Position-Specific Scoring Matrix (PSSM) containing biological evolution information, and then the feature vector is extracted to present protein evolutionary information using DCT algorithm; finally, the ensemble rotation forest model is used to predict whether a given protein pair is interacting or not. When performed on Yeast and H. pylori data sets, the proposed method achieved excellent results with an average accuracy of 98.54% and 88.27%. In addition, we achieved good prediction accuracy of 98.08%, 92.75%, 98.87% and 98.72% on independent data sets (C.elegans, E.coli, H.sapiens and M.musculus). In order to further evaluate the performance of our method, we compare it with the state-of-the-art Support Vector Machine (SVM) classifier and get good results. As a web server, the source code and Yeast data sets used in this article are freely available at http://202.119.201.126:8888/DCTRF/.

High Precision DCT CORDIC Architectures for Maximum PSNR

This paper proposes two optimal Cordic Loeffler based DCT (Discrete Cosine Transform algorithm) architectures: a fast and low Power DCT architecture and a high PSNR DCT architecture. The rotation parameters of CORDIC angles required for these architectures have been calculated using a MATLAB script. This script allows the variation of the angle’s precision from 10-1 to 10-4. The experimental results show that the fast and low Power DCT architecture correponds to the precision 10-1. Its complexity is even lower than the BinDCT which is a reference in terms of low complexity and its power has been enhanced in comparison with the conventional Cordic Loeffler DCT by 12 mW. The experimental results also show that the high PSNR DCT architecture corresponds to the precision 10-3 for which the PSNR has been improved by 6.55 dB in comparison with the conventional Cordic Loeffler DCT. Then, the hardware implementation and the generated RTL of some required Cordics are presented.